Summary attributable risk estimation from unmatched case-control data.

Abstract

We propose an alternative method to obtain summary estimators, variances and confidence intervals for attributable risk measures. This method combines weighted exposure prevalences for cases and controls across strata formed by the cross-classification of relevant covariates to form estimates of attributable risk among the exposed and attributable risk in the target population. The major benefit of this approach over those previously proposed in the literature is that it operates on data summed across strata rather than on estimation of statistics within each stratum. This alternative method for attributable risk measures utilizes the Mantel-Haenszel estimate of an average odds ratio, and can be implemented using the matrix procedure in SAS. This method is appropriate even when the within-stratum sample sizes are too small for other methods to be valid. Simulation results indicate that this method is superior to others with respect to bias and coverage probability for confidence intervals.